Cluster Tracking with Time-of-Flight Cameras

Dan Witzner Hansen, Mads Hansen, Martin Kirschmeyer, Rasmus Larsen, Davide Silvestre

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    We describe a method for tracking people using a time-of-flight camera and apply the method for persistent authentication in a smart-environment. A background model is built by fusing information from intensity and depth images. While a geometric constraint is employed to improve pixel cluster coherence and reducing the influence of noise, the EM algorithm (expectation maximization) is used for tracking moving clusters of pixels significantly different from the background model. Each cluster is defined through a statistical model of points on the ground plane. We show the benefits of the time-of-flight principles for people tracking but also their current limitations.
    Original languageEnglish
    Title of host publicationComputer Vision and Pattern Recognition Workshops : Time-of-flight based computer vision
    PublisherIEEE Computer Society Press
    Publication date2008
    ISBN (Print)978-1-4244-2339-2
    Publication statusPublished - 2008
    Event2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Anchorage, AK, United States
    Duration: 23 Jun 200828 Jun 2008


    Conference2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    CountryUnited States
    CityAnchorage, AK
    Internet address

    Bibliographical note

    Copyright: 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE


    • computer vision
    • action tracking
    • time-of-flight camera

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